class_docstr: Glow Affine Coupling block as in GlowTTS paper.
  https://arxiv.org/pdf/1811.00002.pdf
  
   x --> x0 -> conv1d -> wavenet -> conv1d --> t, s -> concat(s*x1 + t, x0) -> o
     '-> x1 - - - - - - - - - - - - - - - - - - - - - - - - - ^
  
   Args:
       in_channels (int): number of input tensor channels.
       hidden_channels (int): number of hidden channels.
       kernel_size (int): WaveNet filter kernel size.
       dilation_rate (int): rate to increase dilation by each layer in a decoder block.
       num_layers (int): number of WaveNet layers.
       c_in_channels (int): number of conditioning input channels.
       dropout_p (int): wavenet dropout rate.
       sigmoid_scale (bool): enable/disable sigmoid scaling for output scale.
  
   Note:
       It does not use conditional inputs differently from WaveGlow.
   
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code: def store_inverse(self):
      self.wn.remove_weight_norm()
  
file_path: TTS/TTS/tts/layers/glow_tts/glow.py
imports: import torch
  from torch import nn
  from torch.nn import functional
  from TTS.tts.layers.generic.wavenet import WN
  from ..generic.normalization import LayerNorm
label_desc: Use image node_id 3 for calling the CouplingBlock obj's underlying member method code with example usage: obj.store_inverse() without return types